The content introduces an open-loop baseline for locomotion tasks, emphasizing simplicity and leveraging prior knowledge to reduce complexity. By comparing this approach with DRL algorithms, the study reveals insights into performance, robustness, and simulation-to-reality transfer. The ablation study explores design choices' impact on performance, showcasing the effectiveness of phase-dependent frequencies and phase shifts in different environments.
The study demonstrates that the open-loop approach achieves respectable performance across various locomotion tasks with minimal parameters compared to DRL algorithms. It highlights the efficiency, robustness to sensor noise, and successful simulation-to-reality transfer of the proposed baseline. Additionally, it discusses the importance of incorporating domain knowledge into policy design for specific problem categories like locomotion tasks.
Furthermore, an ablation study shows that having phase-dependent frequencies and phase shifts is crucial for optimal performance in different environments. The results suggest that simplicity and leveraging natural dynamics can enhance control strategies for robotic locomotion challenges.
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arxiv.org
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